Abstract
Attributed multi-graphs are data structures to model real-world networks of objects which have rich properties/attributes and they are connected by multiple types of edges. Clustering attributed multi-graphs has several real-world applications, such as recommendation systems and targeted advertisement. In this paper, we propose an efficient method for Clustering Attributed Multi-graphs with Information Ranking, namely CAMIR. We introduce an iterative algorithm that ranks the different vertex attributes and edge-types according to how well they can separate vertices into clusters. The key idea is to consider the ‘agreement’ among the attribute- and edge-types, assuming that two vertex properties ‘agree’ if they produced the same clustering result when used individually. Furthermore, according to the calculated ranks we construct a unified similarity measure, by down-weighting noisy vertex attributes or edge-types that may reduce the clustering accuracy. Finally, to generate the final clusters, we follow a spectral clustering approach, suitable for graph partitioning and detecting arbitrary shaped clusters. In our experiments with synthetic and real-world datasets, we show the superiority of CAMIR over several state-of-the-art clustering methods.
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Also, other types of kernel functions could be used, such as linear and polynomial, thoroughly examined in [21] for machine learning methods.
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Following [7] we use a common \(\lambda \) for all properties. In practice though, \(\lambda = 0.001\) is an appropriate value to control the impact of the other properties, as we observed in our experiments.
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The full DBLP dataset is available online at http://kdl.cs.umass.edu/data/dblp/dblp-info.html.
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Available online at http://code.google.com.
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Acknowledgments
This work was partially supported by the EU Commission in terms of the PaaSport 605193 FP7 project (FP7-SME-2013).
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Papadopoulos, A., Rafailidis, D., Pallis, G., Dikaiakos, M.D. (2015). Clustering Attributed Multi-graphs with Information Ranking. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_29
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